Construction and Assessment of Classification Rules is an accessible book presenting the central issues and placing particular emphasis on comparison, performance assessment and how to match method to application. Some unusual allocation problems are outlined and a detailed discussion of performance assessment is included. The methods used for different application domains, such as parametric method, smoothing methods and recursive partitioning are described. The author reviews different approaches and guides researchers and users to suitable classes of techniques.

The author has written an accessible book presenting the central issues and placing particular emphasis on comparison, performance assessment and how to match method to application. He reviews the different approaches and guides researchers (and the huge class of users and potential users) to suitable classes of techniques. He points the way to more detailed study in "further reading" sections in each chapter.

Statistical pattern recognition is a very active area of study andresearch, which has seen many advances in recent years. New andemerging applications - such as data mining, web searching,multimedia data retrieval, face recognition, and cursivehandwriting recognition - require robust and efficient patternrecognition techniques. Statistical decision making and estimationare regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fullyupdated with new methods, applications and references. It providesa comprehensive introduction to this vibrant area - with materialdrawn from engineering, statistics, computer science and the socialsciences - and covers many application areas, such as databasedesign, artificial neural networks, and decision supportsystems. * Provides a self-contained introduction to statistical patternrecognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vectormachines, and unsupervised classification. * Each section concludes with a description of the applicationsthat have been addressed and with further developments of thetheory. * Includes background material on dissimilarity, parameterestimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions tomore lengthy projects. The book is aimed primarily at senior undergraduate and graduatestudents studying statistical pattern recognition, patternprocessing, neural networks, and data mining, in both statisticsand engineering departments. It is also an excellent source ofreference for technical professionals working in advancedinformation development environments. For further information on the techniques and applicationsdiscussed in this book please visit ahref="http://www.statistical-pattern-recognition.net/"www.statistical-pattern-recognition.net/a

This second edition of the classic textbook, The Archaeologist’s Laboratory, is a substantially revised work that offers updated information on the archaeological work that follows fieldwork, such as the processing and analysis of artifacts and other evidence. An overarching theme of this edition is the quality and validity of archaeological arguments and the data we use to support them. The book introduces many of the laboratory activities that archaeologists carry out and the ways we can present research results, including graphs and artifact illustrations. Part I introduces general topics concerning measurement error, data quality, research design, typology, probability and databases. It also includes data presentation, basic artifact conservation, and laboratory safety. Part II offers brief surveys of the analysis of lithics and ground stone, pottery, metal artifacts, bone and shell artifacts, animal and plant remains, and sediments, as well as dating by stratigraphy, seriation and chronometric methods. It concludes with a chapter on archaeological illustration and publication. A new feature of the book is illustration of concepts through case studies from around the world and from the Palaeolithic to historical archaeology.The text is appropriate for senior undergraduate students and will also serve as a useful reference for graduate students and professional archaeologists.

The Handbook of Massive Data Sets is comprised of articles written by experts on selected topics that deal with some major aspect of massive data sets. It contains chapters on information retrieval both in the internet and in the traditional sense, web crawlers, massive graphs, string processing, data compression, clustering methods, wavelets, optimization, external memory algorithms and data structures, the US national cluster project, high performance computing, data warehouses, data cubes, semi-structured data, data squashing, data quality, billing in the large, fraud detection, and data processing in astrophysics, air pollution, biomolecular data, earth observation and the environment. The proliferation of massive data sets brings with it a series of special computational challenges. This "data avalanche" arises in a wide range of scientific and commercial applications.

Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead. The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM.